Evaluation of a High-Risk Case Management Pilot Program for Medicare Beneficiaries with Medigap Coverage

Hawkins, Kevin; Parker, Paula M.; Hommer, Cynthia E.; Bhattarai, Gandhi R.; Huang, Jinghua; Wells, Timothy S.; Ozminkowski, Ronald J.; Yeh, Charlotte S.

doi:10.1089/pop.2014.0035

Cited by 17 publications

(25 citation statements)

References 29 publications

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“…In a recent evaluation of the AARP high-risk care coordination program component, [16] increased duration in care coordination was associated with fewer hospital readmissions, and participants were significantly more likely to have recurring office visits and recommended laboratory tests. A similar evaluation showed improved quality of care and savings for those who participated in the emergency room decision support component [15].…”

Section: Mycarepath Care Coordinationmentioning

confidence: 99%

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Leveraging big data in population health management

et al. 2016

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Background: Population health management takes into account many determinants of health, including medical care, social and physical environments and related services, genetics, and individual behavior. Many different types of data may be used to guide population health management programs and to estimate program value. In addition to the variety of data required for these programs, big population health program data are characterized by large volume, high velocity, and inconsistent data flows. This manuscript describes how big data analytics have been used to craft a population health program to help improve the lives of about four million older adults who have an AARP® Medicare Supplement Insurance plan (i.e., a Medigap plan). Plan enrollees have access to a wellness program, holistic care coordination programs, two telephone-based advice lines, concierge support for insurance and medical care needs, and a program designed to help reduce unnecessary emergency room (ER) visits. Results: During 2009-2011, these program components led to several improvements in health care. For example, increased duration in care coordination was associated with fewer hospital readmissions, and participants were significantly more likely to have recurring physician office visits and recommended laboratory tests. Participants in ER decision support reduced their ER visits by 1299 visits per 1000 insureds, compared with a reduction of 1121 visits per 1000 insureds for individuals who did not participate in the program. Better depression management helped reduce depression symptoms in 59 % of participants engaged in that program. Big data analytics of member data suggested the need for a wellness program feature, which began in 2014. Analytics of disease management services offered in 2009-2011 helped to combine and refocus these program features to enhance effectiveness in later years. Conclusions: Using big data to help manage and evaluate a population health program has led to several improvements in health care. Program management, reporting, and evaluation processes generated additional data which, when analyzed, continues to refine program implementation and quality. Future improvements to this program may include enhanced integration of social service programs that will generate their own data streams for analyses designed to further improve health and wellbeing.

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Section: Mycarepath Care Coordinationmentioning

confidence: 99%

“…It is staffed by licensed registered nurses who work with plan enrollees to address their personal goals and both medical and non-medical needs. More information about MyCarePath can be found in Hawkins et al [16].…”

Section: Population Health Program Componentsmentioning

confidence: 99%

Leveraging big data in population health management

et al. 2016

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“…The difference-in-difference design is frequently applied to minimize validity threats (Baker et al 2011;Lin et al 2012;Grossmeier et al 2013;Hawkins et al 2015;Cuellar et al 2016). This method estimates the pre-post difference for the intervention group and compares it to the pre-post difference for a similar group not exposed to the intervention.…”

Section: Evaluation Design Considerationsmentioning

confidence: 99%

Key Design Considerations When Calculating Cost Savings for Population Health Management Programs in an Observational Setting

Murphy

Hough

Sylvia

et al. 2018

Health Services Research

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“…Additional details of the regression strategy and its results are documented elsewhere. 15 Estimates of per individual per month program savings or losses are derived from the regression analyses and then multiplied by the total number of months of program participation, to determine how many dollars were saved or lost as a result of participation in MyCarePath.…”

Section: Figmentioning

confidence: 99%

“…Results of the quality of care analytics have also been described elsewhere. 15 To improve outreach efforts for MyCarePath, the results of the program savings and quality of care regression models were used to construct the PTS modeling process, which is described next.…”

Section: Figmentioning

confidence: 99%

Big Data, Little Data, and Care Coordination for Medicare Beneficiaries with Medigap Coverage

et al. 2015

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Most healthcare data warehouses include big data such as health plan, medical, and pharmacy claims information for many thousands and sometimes millions of insured individuals. This makes it possible to identify those with multiple chronic conditions who may benefit from participation in care coordination programs meant to improve their health. The objective of this article is to describe how large databases, including individual and claims data, and other, smaller types of data from surveys and personal interviews, are used to support a care coordination program. The program described in this study was implemented for adults who are generally 65 years of age or older and have an AARP(®) Medicare Supplement Insurance Plan (i.e., a Medigap plan) insured by UnitedHealthcare Insurance Company (or, for New York residents, UnitedHealthcare Insurance Company of New York). Individual and claims data were used first to calculate risk scores that were then utilized to identify the majority of individuals who were qualified for program participation. For efficient use of time and resources, propensity to succeed modeling was used to prioritize referrals based upon their predicted probabilities of (1) engaging in the care coordination program, (2) saving money once engaged, and (3) receiving higher quality of care. To date, program evaluations have reported positive returns on investment and improved quality of healthcare among program participants. In conclusion, the use of data sources big and small can help guide program operations and determine if care coordination programs are working to help older adults live healthier lives.

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Evaluation of a High-Risk Case Management Pilot Program for Medicare Beneficiaries with Medigap Coverage

Cited by 17 publications

References 29 publications

Leveraging big data in population health management

Leveraging big data in population health management

Key Design Considerations When Calculating Cost Savings for Population Health Management Programs in an Observational Setting

Big Data, Little Data, and Care Coordination for Medicare Beneficiaries with Medigap Coverage

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